STM Journals Peer-reviewed research from Consortium e-Learning Network

Research and Reviews : A Journal of Medical Science and Technology Original Research

The Analysis of Deep Learning-Based Methods for Identifying Diabetic Retinopathy

  1. Tshetiz Dahal Department of General Medicine, 16 Lypnia St. Rivne
  2. Suyash Saurabh Department of Physiology, Rajendra Institute of Medical Sciences, Bariatu, Ranchi,

Abstract

Diabetic retinopathy (DR) is a degenerative eye condition resulting from diabetes mellitus, where high blood glucose levels lead to lesions on the retina. This condition is considered the leading cause of blindness among working-age diabetic patients, particularly in developing countries. As the disease is irreversible, the treatment aims to preserve the patient’s current vision. Early detection is crucial for effective management of DR to maintain vision. One of the main challenges in DR detection is the significant time, cost, and effort required for manual diagnosis, which involves having an ophthalmologist review retinal fundus images. The latter also turns out to be more challenging, especially when the sickness is still in its early stages and the signs of the illness are less noticeable in the pictures. Deep learning algorithms have helped in the early identification of DR, and machine learning-based medical image analysis has demonstrated proficiency in evaluating retinal fundus pictures. To propose retinal fundus picture classification and detection, this study discusses and analyzes the most recent deep learning techniques in supervised, self-supervised, and Vision Transformer configurations. For example, a review and summary of the DR referable, non-referable, and proliferative classifications are provided. The study also covers the retinal fundus datasets for DR that are currently accessible and can be used for segmentation, classification, and detection tasks. Along with addressing several issues that require more research and analysis, the paper evaluates research gaps in the field of DR detection and categorization.

Keywords

References (29)

  1. Amin J, Sharif M, Yasmin M. A Review on Recent Developments for Detection of Diabetic Retinopathy. Scientifica. 2016;2016:1-20. doi:10.1155/2016/6838976
  2. Kharroubi AT. Diabetes mellitus: The epidemic of the century. World Journal of Diabetes. 2015;6(6):850. doi:10.4239/wjd.v6.i6.850
  3. Mamtora S, Wong Y, Bell D, Sandinha T. Bilateral Birdshot Retinochoroiditis and Retinal Astrocytoma. Case Reports in Ophthalmological Medicine. 2017;2017:1-4. doi:10.1155/2017/6586157
  4. Yau JWY, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, et al. Global Prevalence and Major Risk Factors of Diabetic Retinopathy. Diabetes Care. 2012;35(3):556-564. doi:10.2337/dc11-1909
  5. Dubow M, Pinhas A, Shah N, Cooper RF, Gan A, Gentile RC, et al. Classification of Human Retinal Microaneurysms Using Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography. Investigative Opthalmology & Visual Science. 2014;55(3):1299. doi:10.1167/iovs.13-13122
  6. Vora P, Shrestha S. Detecting Diabetic Retinopathy Using Embedded Computer Vision. Applied Sciences. 2020;10(20):7274. doi:10.3390/app10207274
  7. Murugesan N, Üstunkaya T, Feener E. Thrombosis and Hemorrhage in Diabetic Retinopathy: A Perspective from an Inflammatory Standpoint. Seminars in Thrombosis and Hemostasis. 2015;41(06):659-664. doi:10.1055/s-0035-1556731
  8. Wong TY, Sun J, Kawasaki R, Ruamviboonsuk P, Gupta N, Lansingh VC, et al. Guidelines on Diabetic Eye Care. Ophthalmology. 2018;125(10):1608-1622. doi:10.1016/j.ophtha.2018.04.007
  9. Birbrair A, Zhang T, Wang ZM, Messi ML, Mintz A, Delbono O. Pericytes at the intersection between tissue regeneration and pathology: Figure 1. Clinical Science. 2015;128(2):81-93. doi:10.1042/cs20140278
  10. Silva C, Bouwmans T, Frélicot C. An eXtended Center-Symmetric Local Binary Pattern for Background Modeling and Subtraction in Videos. Proceedings of the 10th International Conference on Computer Vision Theory and Applications. 2015:395-402. doi:10.5220/0005266303950402
  11. Al Hazaimeh M, Nahar KMO, Al Naami B, Gharaibeh N. An effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images. Int J Signal Imag Syst Eng. 2018;11(4):206. doi:10.1504/IJSISE.2018.10015063.
  12. Fujita H, Uchiyama Y, Nakagawa T, Fukuoka D, Hatanaka Y, Hara T, et al. Computer-aided diagnosis: The emerging of three CAD systems induced by Japanese health care needs. Computer Methods and Programs in Biomedicine. 2008;92(3):238-248. doi:10.1016/j.cmpb.2008.04.003
  13. Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, Algeria, Attia A, Akhtar Z, State University of New York Polytechnic Institute, United States, Akhrouf S, Mohamed Boudiaf University, Algeria, et al. A SURVEY ON MACHINE AND DEEP LEARNING FOR DETECTION OF DIABETIC RETINOPATHY. ICTACT Journal on Image and Video Processing. 2020;11(2):2337-2344. doi:10.21917/ijivp.2020.0332
  14. Gupta R, Chhikara R. Diabetic retinopathy: Present and past. Proc Comput Sci. 2018;132:1432–1440. doi: 1016/j.procs.2018.05.074.
  15. Alyoubi WL, Shalash WM, Abulkhair MF. Diabetic retinopathy detection through deep learning techniques: A review. Informatics in Medicine Unlocked. 2020;20:100377. doi:10.1016/j.imu.2020.100377
  16. Stolte S, Fang R. A survey on medical image analysis in diabetic retinopathy. Medical Image Analysis. 2020;64:101742. doi:10.1016/j.media.2020.101742
  17. Asiri N, Hussain M, Al Adel F, Alzaidi N. Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey. Artificial Intelligence in Medicine. 2019;99:101701. doi:10.1016/j.artmed.2019.07.009
  18. S V, R V. A Survey on Diabetic Retinopathy Disease Detection and Classification using Deep Learning Techniques. 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII). 2021:1-4. doi:10.1109/icbsii51839.2021.9445163
  19. Shamshad F, Khan S, Zamir SW, Khan MH, Hayat M, Khan FS, et al. Transformers in medical imaging: A survey. Medical Image Analysis. 2023;88:102802. doi:10.1016/j.media.2023.102802
  20. Asad H, Azar AT, El-Bendary N, Hassaanien AE. Ant colony-based feature selection heuristics for retinal vessel segmentation. 2014. arXiv:1403.1735 [cs.CV].
  21. Diabetic Retinopathy Detection EYEPACS Dataset. San Francisco, CA, USA. 2015.
  22. Atlanta, GA, USA. 2018 Jun. Available at https://www.kaggle.com/c/aptos2019-blindness-detection
  23. The STARE Project. San Diego, CA, USA. 2004.
  24. Kauppi T, Kalesnykiene V, Kamarainen JK, Lensu L, Sorri I, Uusitalo H, et al. DIARETDB0: Evaluation database and methodology for diabetic retinopathy algorithms. 2007;61–65. doi: 5244/C.21.15.
  25. Giancardo L, Meriaudeau F, Karnowski TP, Li Y, Garg S, Tobin KW, et al. Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Medical Image Analysis. 2012;16(1):216-226. doi:10.1016/j.media.2011.07.004
  26. Niemeijer M, van Ginneken B, Cree MJ, Mizutani A, Quellec G, Sanchez CI, et al. Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs. IEEE Transactions on Medical Imaging. 2010;29(1):185-195. doi:10.1109/tmi.2009.2033909
  27. Porwal P, Pachade S, Kokare M, Deshmukh G, Son J, Bae W, et al. IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge. Medical Image Analysis. 2020;59:101561. doi:10.1016/j.media.2019.101561
  28. Decencière E, Cazuguel G, Zhang X, Thibault G, Klein JC, Meyer F, et al. TeleOphta: Machine learning and image processing methods for teleophthalmology. IRBM. 2013;34(2):196-203. doi:10.1016/j.irbm.2013.01.010
  29. Li T, Gao Y, Wang K, Guo S, Liu H, Kang H. Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Information Sciences. 2019;501:511-522. doi:10.1016/j.ins.2019.06.011
Support